Skip to main content
Log in

Sigma-Point Kalman Filter Algorithm in the Problem of GNSS Signal Parameters Estimation in Non-Coherent Tracking Mode in Spacecraft Autonomous Navigation Equipment

  • Published:
Gyroscopy and Navigation Aims and scope Submit manuscript

Abstract

The paper presents a multiloop system for noncoherent tracking of the radionavigation parameters of global navigation satellite system (GNSS) signals in autonomous satellite navigation system. Comparative analysis of accuracies of traditional tracking system with discriminators and filter in the tracking loop and the proposed system without discriminators is conducted. RMS errors of estimates, pull-in range and the probability of signal acquisition are studied under various SNR values. Experimental tests of derived noncoherent tracking loop are performed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Perov, A.I., and Kharisov, V.N., GLONASS: printsipy postroeniya i funktsionirovaniya (GLONASS: Principles of Construction and Functioning), Moscow: Radiotekhnika, 2010.

    Google Scholar 

  2. Kaplan, E.D., and Hegarty, C.J., Understanding GPS. Principles and Applications, Artech House, 2006, 2nd ed.

    Google Scholar 

  3. Mikhailov, N.V., Avtonomnaya navigatsiya kosmicheskikh apparatov pri pomoshchi sputnikovykh radionavigatsionnykh sistem (Autonomous Navigation of Spacecraft Using Radionavigation Satellite Systems), St. Petersburg: Politekhnika, 2014.

    Google Scholar 

  4. Psiaki, M.L., and Jung, H., Extended Kalman filter methods for tracking weak GPS signals, Proceedings of ION GPS 2002, Portland, USA, 2002, pp. 2539–2553.

    Google Scholar 

  5. Ziedan N.I., and Garrison J.L., Bit synchronization and Doppler frequency removal at very low carrier to noise ratio using a combination of the Viterbi algorithm with an extended Kalman filter, Proceedings of ION GPS 2003, Portland, USA, 2003, pp. 616–627.

    Google Scholar 

  6. Ziedan, N.I., and Garrison, J.L., Extended Kalman filter-based tracking of weak GPS signals under high dynamic conditions, Proceedings of ION GNSS 2004, Long Beach, USA, 2004, pp. 20–31.

    Google Scholar 

  7. Ren, T., Petovello, M.G., and Basnayake, C., Requirements analysis for bit synchronization and decoding in a standalone high-sensitivity GNSS receiver, Proceedings of Ubiquitous Positioning, Indoor Navigation, and Location Based Service (UPINLBS), Helsinki, Finland, 2012, pp. 1–9.

    Google Scholar 

  8. Ding, J., Zhang, G., and Zhao, L., Urban and indoor weak signal tracking using an array tracker with MVA and nonlinear filtering, Journal of Applied Mathematics, 2014.

    Book  Google Scholar 

  9. Petovello, M.G., O’Driscoll, C., and Lachapelle, G., Carrier phase tracking of weak signals using different receiver architectures, Proceedings of ION NTM 2008, San Diego, CA, 2008.

    Google Scholar 

  10. Perov, A.I., and Korogodin, I.V., Synthesis and analysis of algorithms for estimating the power of the signal and the noise components at the correlator’s output, Radiotekhnika, 2011, no. 7, pp. 76–82.

    Google Scholar 

  11. Falletti, E., Pini, M., Lo Presti, L., GNSS solutions: carrier-to-noise algorithms, Inside GNSS, 2010, Jan/Feb, pp. 20–27.

    Google Scholar 

  12. Shavrin, V.V., Filimonov, V.A., Lebedev, V.Yu., Tislenko, V.I., Kravets, A.P., and Konakov, A.S., Quasioptimal estimation of GNSS signal parameters in coherent reception mode using sigma-point Kalman filter, Gyroscopy and Navigation, 2017, vol. 8, no. 1, pp. 24–30.

    Article  Google Scholar 

  13. Sarkka, S., Bayesian Filtering and Smoothing, Cambridge University Press, 2013.

    Book  MATH  Google Scholar 

  14. Tikhonov, V.I., and Kharisov, V.N., Statisticheskii analiz i sintez radiotekhnicheskikh ustroistv i sistem (Statistical Analysis and Synthesis of Radiotechnical Devices and Systems), Moscow: Radio i svyaz', 1991.

    Google Scholar 

  15. Simandl, M., Lecture Notes on State Estimation of Nonlinear Non-Gaussian Stochastic Systems, Pilsen: University of West Bohemia, 2006.

    Google Scholar 

  16. Im, S., Song, J., Jee, G., and Park, C., Comparison of GPS tracking loop performance in high dynamic condition with nonlinear filtering techniques, ION GNSS 21st International Technical Meeting of Satellite Division, 2008. pp 2351–2360.

    Google Scholar 

  17. Korogodin, I.V., Potential performance of frequency estimation for non-coherent receiver, Radiotekhnika, 2013, no. 7, pp. 109–115.

    Google Scholar 

  18. Boldenkov, E.N., Complex delay and frequency of GNSS signal tracking algorithm based on optimal trajectory filtering techniques, Radiotekhnika, 2013, no. 10, pp. 103–106.

    Google Scholar 

  19. Gonorovskii, I.S., Radiotekhnicheskie tsepi i signaly (Radiotechnical Circuits and Signals): uchebnik dlya vuzov (Textbook for Higher Educational Establishments), 4th ed., Moscow: Radio i svyaz', 1986.

    Google Scholar 

  20. Doucet, A., and Johansen A., A tutorial on particle filtering and smoothing: fifteen years later. In: Crisan, D., Rozovsky, B. (eds.) Oxford Handbook of Nonlinear Filtering, OUP, Oxford, 2009.

    MATH  Google Scholar 

  21. Merwe, R., Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models, PhD Thesis, 2004.

    Google Scholar 

  22. Candy, J.V., Bayesian Signal Processing. Classical, Modern, and Particle Filtering Methods, John Wiley & Sons, Inc., 2009.

    Google Scholar 

  23. Julier, S.J., and Uhlman, J.K., A new extension of the Kalman filter to nonlinear systems, Proc. of AeroSence, the 11th Intern. Symp. On Aerospace/Defence Sensing, Simulation and Controls, Orlando FL, USA. 1997.

    Google Scholar 

  24. Borre, K., Akos, D.M., Bertelsen, N., Rinder, P., and Jensen, S.H., A Software-Defined GPS and Galileo Receiver. A Single-Frequency Approach, Boston: Birkhauser, 2007.

    MATH  Google Scholar 

  25. https://doi.org/www.datatec.de/shop/artikelpdf/n5182b_d.pdf, Keysight Technologies, MXG X-Series Signal Generators, N5181B Analog & N5182B Vector, Datasheet, accessed 23.07.2018.

  26. Juang J.-C., and Chen Y.-H., Phase/frequency tracking in a GNSS software receiver, IEEE Journal of Selected Topics in Signal Processing, 2009, no. 3 (4), pp. 651–660.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to V. V. Shavrin.

Additional information

Original Russian Text © V.V. Shavrin, V.I. Tislenko, V.Yu. Lebedev, V.A. Filimonov, A.S. Konakov, 2018, published in Giroskopiya i Navigatsiya, 2018, No. 3, pp. 23–39.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shavrin, V.V., Tislenko, V.I., Lebedev, V.Y. et al. Sigma-Point Kalman Filter Algorithm in the Problem of GNSS Signal Parameters Estimation in Non-Coherent Tracking Mode in Spacecraft Autonomous Navigation Equipment. Gyroscopy Navig. 9, 255–266 (2018). https://doi.org/10.1134/S2075108718040089

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S2075108718040089

Keywords

Navigation